| | |
| | | |
| | | split_scps_tool=split_scp.pl |
| | | inference_tool=infer.py |
| | | proce_text_tool=proce_text.py |
| | | compute_wer_tool=compute_wer.py |
| | | |
| | | nj=32 |
| | | stage=0 |
| | | stop_stage=2 |
| | |
| | | scp="/nfs/haoneng.lhn/funasr_data/aishell-1/data/test/wav.scp" |
| | | label_text="/nfs/haoneng.lhn/funasr_data/aishell-1/data/test/text" |
| | | export_root="/nfs/zhifu.gzf/export" |
| | | split_scps_tool=split_scp.pl |
| | | inference_tool=infer.py |
| | | proce_text_tool=proce_text.py |
| | | compute_wer_tool=compute_wer.py |
| | | |
| | | |
| | | #:<<! |
| | | model_name="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" |
| | |
| | | for JOB in $(seq ${nj}); do |
| | | { |
| | | core_id=`expr $JOB - 1` |
| | | taskset -c ${core_id} python ${rtf_tool} --backend ${backend} --model_dir ${model_dir} --wav_file ${output_dir}/wav.$JOB.scp --quantize ${quantize} --output_dir ${output_dir}/${JOB} &> ${output_dir}/log.$JOB.txt |
| | | taskset -c ${core_id} python ${inference_tool} --backend ${backend} --model_dir ${model_dir} --wav_file ${output_dir}/wav.$JOB.scp --quantize ${quantize} --output_dir ${output_dir}/${JOB} &> ${output_dir}/log.$JOB.txt |
| | | }& |
| | | |
| | | done |
| | |
| | | for f in token text; do |
| | | if [ -f "${output_dir}/1/${f}" ]; then |
| | | for JOB in $(seq "${nj}"); do |
| | | cat "${output_dir}/${JOB}/1best_recog/${f}" |
| | | cat "${output_dir}/${JOB}/${f}" |
| | | done | sort -k1 >"${output_dir}/1best_recog/${f}" |
| | | fi |
| | | done |